Multi-model fitting based on minimum spanning tree

Radwa Fathalla, George Vogiatzis

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

This paper presents a novel approach to the computation of primitive geometrical structures, where no prior knowledge about the visual scene is available and a high level of noise is expected. We based our work on the grouping principles of proximity and similarity, of points and preliminary models. The former was realized using Minimum Spanning Trees (MST), on which we apply a stable alignment and goodness of fit criteria. As for the latter, we used spectral clustering of preliminary models. The algorithm can be generalized to various model fitting settings, without tuning of run parameters. Experiments demonstrate the significant improvement in the localization accuracy of models in plane, homography and motion segmentation examples. The efficiency of the algorithm is not dependent on fine tuning of run parameters like most others in the field.

Original languageEnglish
Title of host publicationBMVC 2014 : proceedings of the British Machine Vision Conference 2014
EditorsMichel Valstar, Andrew French, Tony Pridmore
Number of pages12
Publication statusPublished - 30 Sep 2014
Event25th British Machine Vision Conference - Nottingham, United Kingdom
Duration: 1 Sep 20145 Sep 2014

Conference

Conference25th British Machine Vision Conference
Abbreviated titleBMVC 2014
CountryUnited Kingdom
CityNottingham
Period1/09/145/09/14

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    Fathalla, R., & Vogiatzis, G. (2014). Multi-model fitting based on minimum spanning tree. In M. Valstar, A. French, & T. Pridmore (Eds.), BMVC 2014 : proceedings of the British Machine Vision Conference 2014 http://www.bmva.org/bmvc/2014/files/paper122.pdf